trends, major applications, and performance evaluations in crop management, yield prediction,
and resource efficiency.
Recent studies have also emphasized the integration of remote sensing data with advanced
computational techniques. Lu (2023) introduces a multi-scale feature fusion semantic
segmentation model for crop classification using high-resolution remote sensing imagery. By
combining spatial and spectral data, the model improves classification accuracy and efficiently
differentiates between crop types. Sayago (2018) presents a similar approach, leveraging multi-
scale feature fusion to enhance precision in crop categorization.
Satellite imagery has been a fundamental component in agricultural forecasting. Sabini (2017)
explores the potential of satellite-based data to improve crop production predictions, particularly
by integrating machine learning techniques with remote sensing information. In a more recent
study, Olisah (2024) develops a deep neural network model for corn production forecasting,
aimed at assisting smallholder farmers in making informed decisions. By improving yield
forecasting accuracy, the model facilitates effective resource allocation and sustainable
agricultural planning.
Accurate crop yield prediction relies on the effective classification and clustering of agricultural
fields using remote sensing data. While machine learning and deep learning models have
significantly improved yield forecasting, challenges arise when dealing with small-scale
farmlands where spatial granularity is reduced. In remote sensing, clustering crop fields is
relatively straightforward for large agricultural plots due to their distinct spectral and spatial
characteristics. However, traditional clustering techniques face challenges in fragmented or
smallholder farming regions to distinguish between individual fields, thus leading to errors in
classification and reduced model accuracy.
Our work addresses this challenge by developing a clustering approach specific to small-scale
agricultural landscapes. Rather than using K-means or DBSCAN that tend to break in high-
granularity settings, we adopt the use of more advanced methods integrating multi-scale feature
extraction from remote sensing data. Using Sentinel-2 imagery, we hope to apply the benefits of
multi-modal deep learning frameworks to further improve the separation of closely located crop
fields. We also examine hybrid methods of clustering that combine spatial and spectral
information to enhance the precision in segmenting smallholder farm regions.
One of the prime features of our approach is applying clustering techniques depending on
varying sizes of fields dynamically by adjusting granularity in features. Large crop fields have
uniform responses in terms of spectral response but smaller fields can have mixed signals due to
different crop types near them, changes in soil characteristics, and sometimes irrigation patterns